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<div class="header">
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<a href="#pub-methods">Public 成员函数</a> &#124;
<a href="#pub-static-attribs">静态 Public 属性</a> &#124;
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<div class="title">pcl::GaussianKernel类 参考<div class="ingroups"><a class="el" href="group__common.html">Common components</a></div></div>  </div>
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<p><code>#include &lt;<a class="el" href="gaussian_8h_source.html">gaussian.h</a>&gt;</code></p>
<table class="memberdecls">
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="pub-methods"></a>
Public 成员函数</h2></td></tr>
<tr class="memitem:a1c5984a5ba78f8277a03eea307dfda2b"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_gaussian_kernel.html#a1c5984a5ba78f8277a03eea307dfda2b">compute</a> (float sigma, Eigen::VectorXf &amp;<a class="el" href="classpcl_1_1kernel.html">kernel</a>, unsigned kernel_width=MAX_KERNEL_WIDTH) const</td></tr>
<tr class="separator:a1c5984a5ba78f8277a03eea307dfda2b"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a663729cdfd1e440563030e29215f128b"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_gaussian_kernel.html#a663729cdfd1e440563030e29215f128b">compute</a> (float sigma, Eigen::VectorXf &amp;<a class="el" href="classpcl_1_1kernel.html">kernel</a>, Eigen::VectorXf &amp;derivative, unsigned kernel_width=MAX_KERNEL_WIDTH) const</td></tr>
<tr class="separator:a663729cdfd1e440563030e29215f128b"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:ad01d1002116c470278a9f4e4330eed6f"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_gaussian_kernel.html#ad01d1002116c470278a9f4e4330eed6f">convolveRows</a> (const <a class="el" href="classpcl_1_1_point_cloud.html">pcl::PointCloud</a>&lt; float &gt; &amp;input, const Eigen::VectorXf &amp;<a class="el" href="classpcl_1_1kernel.html">kernel</a>, <a class="el" href="classpcl_1_1_point_cloud.html">pcl::PointCloud</a>&lt; float &gt; &amp;output) const</td></tr>
<tr class="separator:ad01d1002116c470278a9f4e4330eed6f"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a49ded4b4344a266ca64a76e9d7dee306"><td class="memTemplParams" colspan="2">template&lt;typename PointT &gt; </td></tr>
<tr class="memitem:a49ded4b4344a266ca64a76e9d7dee306"><td class="memTemplItemLeft" align="right" valign="top">void&#160;</td><td class="memTemplItemRight" valign="bottom"><a class="el" href="classpcl_1_1_gaussian_kernel.html#a49ded4b4344a266ca64a76e9d7dee306">convolveRows</a> (const <a class="el" href="classpcl_1_1_point_cloud.html">pcl::PointCloud</a>&lt; <a class="el" href="structpcl_1_1_point_x_y_z_r_g_b_a.html">PointT</a> &gt; &amp;input, boost::function&lt; float(const <a class="el" href="structpcl_1_1_point_x_y_z_r_g_b_a.html">PointT</a> &amp;p)&gt; field_accessor, const Eigen::VectorXf &amp;<a class="el" href="classpcl_1_1kernel.html">kernel</a>, <a class="el" href="classpcl_1_1_point_cloud.html">pcl::PointCloud</a>&lt; float &gt; &amp;output) const</td></tr>
<tr class="separator:a49ded4b4344a266ca64a76e9d7dee306"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a13fed06781b7735db1d9461a1c507305"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_gaussian_kernel.html#a13fed06781b7735db1d9461a1c507305">convolveCols</a> (const <a class="el" href="classpcl_1_1_point_cloud.html">pcl::PointCloud</a>&lt; float &gt; &amp;input, const Eigen::VectorXf &amp;<a class="el" href="classpcl_1_1kernel.html">kernel</a>, <a class="el" href="classpcl_1_1_point_cloud.html">pcl::PointCloud</a>&lt; float &gt; &amp;output) const</td></tr>
<tr class="separator:a13fed06781b7735db1d9461a1c507305"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a8da5d1e51b46abcc9026006ec4eed4ba"><td class="memTemplParams" colspan="2">template&lt;typename PointT &gt; </td></tr>
<tr class="memitem:a8da5d1e51b46abcc9026006ec4eed4ba"><td class="memTemplItemLeft" align="right" valign="top">void&#160;</td><td class="memTemplItemRight" valign="bottom"><a class="el" href="classpcl_1_1_gaussian_kernel.html#a8da5d1e51b46abcc9026006ec4eed4ba">convolveCols</a> (const <a class="el" href="classpcl_1_1_point_cloud.html">pcl::PointCloud</a>&lt; <a class="el" href="structpcl_1_1_point_x_y_z_r_g_b_a.html">PointT</a> &gt; &amp;input, boost::function&lt; float(const <a class="el" href="structpcl_1_1_point_x_y_z_r_g_b_a.html">PointT</a> &amp;p)&gt; field_accessor, const Eigen::VectorXf &amp;<a class="el" href="classpcl_1_1kernel.html">kernel</a>, <a class="el" href="classpcl_1_1_point_cloud.html">pcl::PointCloud</a>&lt; float &gt; &amp;output) const</td></tr>
<tr class="separator:a8da5d1e51b46abcc9026006ec4eed4ba"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a48fcd11627d7597a27ce4f8984c9dea0"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_gaussian_kernel.html#a48fcd11627d7597a27ce4f8984c9dea0">convolve</a> (const <a class="el" href="classpcl_1_1_point_cloud.html">pcl::PointCloud</a>&lt; float &gt; &amp;input, const Eigen::VectorXf &amp;horiz_kernel, const Eigen::VectorXf &amp;vert_kernel, <a class="el" href="classpcl_1_1_point_cloud.html">pcl::PointCloud</a>&lt; float &gt; &amp;output) const</td></tr>
<tr class="separator:a48fcd11627d7597a27ce4f8984c9dea0"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a488e6649dba1a82fcdf9414ac8aa58bc"><td class="memTemplParams" colspan="2">template&lt;typename PointT &gt; </td></tr>
<tr class="memitem:a488e6649dba1a82fcdf9414ac8aa58bc"><td class="memTemplItemLeft" align="right" valign="top">void&#160;</td><td class="memTemplItemRight" valign="bottom"><a class="el" href="classpcl_1_1_gaussian_kernel.html#a488e6649dba1a82fcdf9414ac8aa58bc">convolve</a> (const <a class="el" href="classpcl_1_1_point_cloud.html">pcl::PointCloud</a>&lt; <a class="el" href="structpcl_1_1_point_x_y_z_r_g_b_a.html">PointT</a> &gt; &amp;input, boost::function&lt; float(const <a class="el" href="structpcl_1_1_point_x_y_z_r_g_b_a.html">PointT</a> &amp;p)&gt; field_accessor, const Eigen::VectorXf &amp;horiz_kernel, const Eigen::VectorXf &amp;vert_kernel, <a class="el" href="classpcl_1_1_point_cloud.html">pcl::PointCloud</a>&lt; float &gt; &amp;output) const</td></tr>
<tr class="separator:a488e6649dba1a82fcdf9414ac8aa58bc"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:ae9d18071f99654164697503aa823d081"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_gaussian_kernel.html#ae9d18071f99654164697503aa823d081">computeGradients</a> (const <a class="el" href="classpcl_1_1_point_cloud.html">pcl::PointCloud</a>&lt; float &gt; &amp;input, const Eigen::VectorXf &amp;gaussian_kernel, const Eigen::VectorXf &amp;gaussian_kernel_derivative, <a class="el" href="classpcl_1_1_point_cloud.html">pcl::PointCloud</a>&lt; float &gt; &amp;grad_x, <a class="el" href="classpcl_1_1_point_cloud.html">pcl::PointCloud</a>&lt; float &gt; &amp;grad_y) const</td></tr>
<tr class="separator:ae9d18071f99654164697503aa823d081"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:aabf2e68d86f1b5628a9976ac8a83ab47"><td class="memTemplParams" colspan="2">template&lt;typename PointT &gt; </td></tr>
<tr class="memitem:aabf2e68d86f1b5628a9976ac8a83ab47"><td class="memTemplItemLeft" align="right" valign="top">void&#160;</td><td class="memTemplItemRight" valign="bottom"><a class="el" href="classpcl_1_1_gaussian_kernel.html#aabf2e68d86f1b5628a9976ac8a83ab47">computeGradients</a> (const <a class="el" href="classpcl_1_1_point_cloud.html">pcl::PointCloud</a>&lt; <a class="el" href="structpcl_1_1_point_x_y_z_r_g_b_a.html">PointT</a> &gt; &amp;input, boost::function&lt; float(const <a class="el" href="structpcl_1_1_point_x_y_z_r_g_b_a.html">PointT</a> &amp;p)&gt; field_accessor, const Eigen::VectorXf &amp;gaussian_kernel, const Eigen::VectorXf &amp;gaussian_kernel_derivative, <a class="el" href="classpcl_1_1_point_cloud.html">pcl::PointCloud</a>&lt; float &gt; &amp;grad_x, <a class="el" href="classpcl_1_1_point_cloud.html">pcl::PointCloud</a>&lt; float &gt; &amp;grad_y) const</td></tr>
<tr class="separator:aabf2e68d86f1b5628a9976ac8a83ab47"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a969fb903ac4abd105fead44ed59cfb49"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_gaussian_kernel.html#a969fb903ac4abd105fead44ed59cfb49">smooth</a> (const <a class="el" href="classpcl_1_1_point_cloud.html">pcl::PointCloud</a>&lt; float &gt; &amp;input, const Eigen::VectorXf &amp;gaussian_kernel, <a class="el" href="classpcl_1_1_point_cloud.html">pcl::PointCloud</a>&lt; float &gt; &amp;output) const</td></tr>
<tr class="separator:a969fb903ac4abd105fead44ed59cfb49"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a4ed8cb8b338735612d4e33a2f035aad9"><td class="memTemplParams" colspan="2">template&lt;typename PointT &gt; </td></tr>
<tr class="memitem:a4ed8cb8b338735612d4e33a2f035aad9"><td class="memTemplItemLeft" align="right" valign="top">void&#160;</td><td class="memTemplItemRight" valign="bottom"><a class="el" href="classpcl_1_1_gaussian_kernel.html#a4ed8cb8b338735612d4e33a2f035aad9">smooth</a> (const <a class="el" href="classpcl_1_1_point_cloud.html">pcl::PointCloud</a>&lt; <a class="el" href="structpcl_1_1_point_x_y_z_r_g_b_a.html">PointT</a> &gt; &amp;input, boost::function&lt; float(const <a class="el" href="structpcl_1_1_point_x_y_z_r_g_b_a.html">PointT</a> &amp;p)&gt; field_accessor, const Eigen::VectorXf &amp;gaussian_kernel, <a class="el" href="classpcl_1_1_point_cloud.html">pcl::PointCloud</a>&lt; float &gt; &amp;output) const</td></tr>
<tr class="separator:a4ed8cb8b338735612d4e33a2f035aad9"><td class="memSeparator" colspan="2">&#160;</td></tr>
</table><table class="memberdecls">
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="pub-static-attribs"></a>
静态 Public 属性</h2></td></tr>
<tr class="memitem:a6d9ac9e0951a53df215bbac90b8b320c"><td class="memItemLeft" align="right" valign="top"><a id="a6d9ac9e0951a53df215bbac90b8b320c"></a>
static const unsigned&#160;</td><td class="memItemRight" valign="bottom"><b>MAX_KERNEL_WIDTH</b> = 71</td></tr>
<tr class="separator:a6d9ac9e0951a53df215bbac90b8b320c"><td class="memSeparator" colspan="2">&#160;</td></tr>
</table>
<a name="details" id="details"></a><h2 class="groupheader">详细描述</h2>
<div class="textblock"><p>Class <a class="el" href="classpcl_1_1_gaussian_kernel.html">GaussianKernel</a> assembles all the method for computing, convolving, smoothing, gradients computing an image using a gaussian kernel. The image is stored in point cloud elements intensity member or rgb or... </p><dl class="section author"><dt>作者</dt><dd>Nizar Sallem </dd></dl>
</div><h2 class="groupheader">成员函数说明</h2>
<a id="a663729cdfd1e440563030e29215f128b"></a>
<h2 class="memtitle"><span class="permalink"><a href="#a663729cdfd1e440563030e29215f128b">&#9670;&nbsp;</a></span>compute() <span class="overload">[1/2]</span></h2>

<div class="memitem">
<div class="memproto">
      <table class="memname">
        <tr>
          <td class="memname">void pcl::GaussianKernel::compute </td>
          <td>(</td>
          <td class="paramtype">float&#160;</td>
          <td class="paramname"><em>sigma</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">Eigen::VectorXf &amp;&#160;</td>
          <td class="paramname"><em>kernel</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">Eigen::VectorXf &amp;&#160;</td>
          <td class="paramname"><em>derivative</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">unsigned&#160;</td>
          <td class="paramname"><em>kernel_width</em> = <code>MAX_KERNEL_WIDTH</code>&#160;</td>
        </tr>
        <tr>
          <td></td>
          <td>)</td>
          <td></td><td> const</td>
        </tr>
      </table>
</div><div class="memdoc">
<p>Computes the gaussian kernel and dervative assiociated to sigma. The kernel and derivative width are adjusted according. </p><dl class="params"><dt>参数</dt><dd>
  <table class="params">
    <tr><td class="paramdir">[in]</td><td class="paramname">sigma</td><td></td></tr>
    <tr><td class="paramdir">[out]</td><td class="paramname">kernel</td><td>the computed gaussian kernel </td></tr>
    <tr><td class="paramdir">[out]</td><td class="paramname">derivative</td><td>the computed kernel derivative </td></tr>
    <tr><td class="paramdir">[in]</td><td class="paramname">kernel_width</td><td>the desired kernel width upper bond </td></tr>
  </table>
  </dd>
</dl>
<dl class="exception"><dt>异常</dt><dd>
  <table class="exception">
    <tr><td class="paramname"><a class="el" href="classpcl_1_1_kernel_width_too_small_exception.html" title="An exception that is thrown when the kernel size is too small">pcl::KernelWidthTooSmallException</a></td><td></td></tr>
  </table>
  </dd>
</dl>

</div>
</div>
<a id="a1c5984a5ba78f8277a03eea307dfda2b"></a>
<h2 class="memtitle"><span class="permalink"><a href="#a1c5984a5ba78f8277a03eea307dfda2b">&#9670;&nbsp;</a></span>compute() <span class="overload">[2/2]</span></h2>

<div class="memitem">
<div class="memproto">
      <table class="memname">
        <tr>
          <td class="memname">void pcl::GaussianKernel::compute </td>
          <td>(</td>
          <td class="paramtype">float&#160;</td>
          <td class="paramname"><em>sigma</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">Eigen::VectorXf &amp;&#160;</td>
          <td class="paramname"><em>kernel</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">unsigned&#160;</td>
          <td class="paramname"><em>kernel_width</em> = <code>MAX_KERNEL_WIDTH</code>&#160;</td>
        </tr>
        <tr>
          <td></td>
          <td>)</td>
          <td></td><td> const</td>
        </tr>
      </table>
</div><div class="memdoc">
<p>Computes the gaussian kernel and dervative assiociated to sigma. The kernel and derivative width are adjusted according. </p><dl class="params"><dt>参数</dt><dd>
  <table class="params">
    <tr><td class="paramdir">[in]</td><td class="paramname">sigma</td><td></td></tr>
    <tr><td class="paramdir">[out]</td><td class="paramname">kernel</td><td>the computed gaussian kernel </td></tr>
    <tr><td class="paramdir">[in]</td><td class="paramname">kernel_width</td><td>the desired kernel width upper bond </td></tr>
  </table>
  </dd>
</dl>
<dl class="exception"><dt>异常</dt><dd>
  <table class="exception">
    <tr><td class="paramname"><a class="el" href="classpcl_1_1_kernel_width_too_small_exception.html" title="An exception that is thrown when the kernel size is too small">pcl::KernelWidthTooSmallException</a></td><td></td></tr>
  </table>
  </dd>
</dl>

</div>
</div>
<a id="ae9d18071f99654164697503aa823d081"></a>
<h2 class="memtitle"><span class="permalink"><a href="#ae9d18071f99654164697503aa823d081">&#9670;&nbsp;</a></span>computeGradients() <span class="overload">[1/2]</span></h2>

<div class="memitem">
<div class="memproto">
<table class="mlabels">
  <tr>
  <td class="mlabels-left">
      <table class="memname">
        <tr>
          <td class="memname">void pcl::GaussianKernel::computeGradients </td>
          <td>(</td>
          <td class="paramtype">const <a class="el" href="classpcl_1_1_point_cloud.html">pcl::PointCloud</a>&lt; float &gt; &amp;&#160;</td>
          <td class="paramname"><em>input</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">const Eigen::VectorXf &amp;&#160;</td>
          <td class="paramname"><em>gaussian_kernel</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">const Eigen::VectorXf &amp;&#160;</td>
          <td class="paramname"><em>gaussian_kernel_derivative</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype"><a class="el" href="classpcl_1_1_point_cloud.html">pcl::PointCloud</a>&lt; float &gt; &amp;&#160;</td>
          <td class="paramname"><em>grad_x</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype"><a class="el" href="classpcl_1_1_point_cloud.html">pcl::PointCloud</a>&lt; float &gt; &amp;&#160;</td>
          <td class="paramname"><em>grad_y</em>&#160;</td>
        </tr>
        <tr>
          <td></td>
          <td>)</td>
          <td></td><td> const</td>
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<p>Computes float image gradients using a gaussian kernel and gaussian kernel derivative. </p><dl class="params"><dt>参数</dt><dd>
  <table class="params">
    <tr><td class="paramdir">[in]</td><td class="paramname">input</td><td>image to compute gardients for </td></tr>
    <tr><td class="paramdir">[in]</td><td class="paramname">gaussian_kernel</td><td>the gaussian kernel to be used </td></tr>
    <tr><td class="paramdir">[in]</td><td class="paramname">gaussian_kernel_derivative</td><td>the associated derivative </td></tr>
    <tr><td class="paramdir">[out]</td><td class="paramname">grad_x</td><td>gradient along X direction </td></tr>
    <tr><td class="paramdir">[out]</td><td class="paramname">grad_y</td><td>gradient along Y direction </td></tr>
  </table>
  </dd>
</dl>
<dl class="section note"><dt>注解</dt><dd>if output doesn't fit in input i.e. output.rows () &lt; input.rows () or output.cols () &lt; input.cols () then output is resized to input sizes. </dd></dl>
<div class="fragment"><div class="line"><a name="l00202"></a><span class="lineno">  202</span>&#160;      {</div>
<div class="line"><a name="l00203"></a><span class="lineno">  203</span>&#160;        <a class="code" href="classpcl_1_1_gaussian_kernel.html#a48fcd11627d7597a27ce4f8984c9dea0">convolve</a> (input, gaussian_kernel_derivative, gaussian_kernel, grad_x);</div>
<div class="line"><a name="l00204"></a><span class="lineno">  204</span>&#160;        <a class="code" href="classpcl_1_1_gaussian_kernel.html#a48fcd11627d7597a27ce4f8984c9dea0">convolve</a> (input, gaussian_kernel, gaussian_kernel_derivative, grad_y);</div>
<div class="line"><a name="l00205"></a><span class="lineno">  205</span>&#160;      }</div>
<div class="ttc" id="aclasspcl_1_1_gaussian_kernel_html_a48fcd11627d7597a27ce4f8984c9dea0"><div class="ttname"><a href="classpcl_1_1_gaussian_kernel.html#a48fcd11627d7597a27ce4f8984c9dea0">pcl::GaussianKernel::convolve</a></div><div class="ttdeci">void convolve(const pcl::PointCloud&lt; float &gt; &amp;input, const Eigen::VectorXf &amp;horiz_kernel, const Eigen::VectorXf &amp;vert_kernel, pcl::PointCloud&lt; float &gt; &amp;output) const</div><div class="ttdef"><b>Definition:</b> gaussian.h:151</div></div>
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<h2 class="memtitle"><span class="permalink"><a href="#aabf2e68d86f1b5628a9976ac8a83ab47">&#9670;&nbsp;</a></span>computeGradients() <span class="overload">[2/2]</span></h2>

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template&lt;typename PointT &gt; </div>
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          <td class="memname">void pcl::GaussianKernel::computeGradients </td>
          <td>(</td>
          <td class="paramtype">const <a class="el" href="classpcl_1_1_point_cloud.html">pcl::PointCloud</a>&lt; <a class="el" href="structpcl_1_1_point_x_y_z_r_g_b_a.html">PointT</a> &gt; &amp;&#160;</td>
          <td class="paramname"><em>input</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">boost::function&lt; float(const <a class="el" href="structpcl_1_1_point_x_y_z_r_g_b_a.html">PointT</a> &amp;p)&gt;&#160;</td>
          <td class="paramname"><em>field_accessor</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">const Eigen::VectorXf &amp;&#160;</td>
          <td class="paramname"><em>gaussian_kernel</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">const Eigen::VectorXf &amp;&#160;</td>
          <td class="paramname"><em>gaussian_kernel_derivative</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype"><a class="el" href="classpcl_1_1_point_cloud.html">pcl::PointCloud</a>&lt; float &gt; &amp;&#160;</td>
          <td class="paramname"><em>grad_x</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype"><a class="el" href="classpcl_1_1_point_cloud.html">pcl::PointCloud</a>&lt; float &gt; &amp;&#160;</td>
          <td class="paramname"><em>grad_y</em>&#160;</td>
        </tr>
        <tr>
          <td></td>
          <td>)</td>
          <td></td><td> const</td>
        </tr>
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<p>Computes float image gradients using a gaussian kernel and gaussian kernel derivative. </p><dl class="params"><dt>参数</dt><dd>
  <table class="params">
    <tr><td class="paramdir">[in]</td><td class="paramname">input</td><td>image to compute gardients for </td></tr>
    <tr><td class="paramdir">[in]</td><td class="paramname">field_accessor</td><td>a field accessor </td></tr>
    <tr><td class="paramdir">[in]</td><td class="paramname">gaussian_kernel</td><td>the gaussian kernel to be used </td></tr>
    <tr><td class="paramdir">[in]</td><td class="paramname">gaussian_kernel_derivative</td><td>the associated derivative </td></tr>
    <tr><td class="paramdir">[out]</td><td class="paramname">grad_x</td><td>gradient along X direction </td></tr>
    <tr><td class="paramdir">[out]</td><td class="paramname">grad_y</td><td>gradient along Y direction </td></tr>
  </table>
  </dd>
</dl>
<dl class="section note"><dt>注解</dt><dd>if output doesn't fit in input i.e. output.rows () &lt; input.rows () or output.cols () &lt; input.cols () then output is resized to input sizes. </dd></dl>
<div class="fragment"><div class="line"><a name="l00225"></a><span class="lineno">  225</span>&#160;      {</div>
<div class="line"><a name="l00226"></a><span class="lineno">  226</span>&#160;        convolve&lt;PointT&gt; (input, field_accessor, gaussian_kernel_derivative, gaussian_kernel, grad_x);</div>
<div class="line"><a name="l00227"></a><span class="lineno">  227</span>&#160;        convolve&lt;PointT&gt; (input, field_accessor, gaussian_kernel, gaussian_kernel_derivative, grad_y);</div>
<div class="line"><a name="l00228"></a><span class="lineno">  228</span>&#160;      }</div>
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<h2 class="memtitle"><span class="permalink"><a href="#a48fcd11627d7597a27ce4f8984c9dea0">&#9670;&nbsp;</a></span>convolve() <span class="overload">[1/2]</span></h2>

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          <td class="memname">void pcl::GaussianKernel::convolve </td>
          <td>(</td>
          <td class="paramtype">const <a class="el" href="classpcl_1_1_point_cloud.html">pcl::PointCloud</a>&lt; float &gt; &amp;&#160;</td>
          <td class="paramname"><em>input</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">const Eigen::VectorXf &amp;&#160;</td>
          <td class="paramname"><em>horiz_kernel</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">const Eigen::VectorXf &amp;&#160;</td>
          <td class="paramname"><em>vert_kernel</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype"><a class="el" href="classpcl_1_1_point_cloud.html">pcl::PointCloud</a>&lt; float &gt; &amp;&#160;</td>
          <td class="paramname"><em>output</em>&#160;</td>
        </tr>
        <tr>
          <td></td>
          <td>)</td>
          <td></td><td> const</td>
        </tr>
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<p>Convolve a float image in the 2 directions </p><dl class="params"><dt>参数</dt><dd>
  <table class="params">
    <tr><td class="paramdir">[in]</td><td class="paramname">horiz_kernel</td><td>kernel for convolving rows </td></tr>
    <tr><td class="paramdir">[in]</td><td class="paramname">vert_kernel</td><td>kernel for convolving columns </td></tr>
    <tr><td class="paramdir">[in]</td><td class="paramname">input</td><td>image to convolve </td></tr>
    <tr><td class="paramdir">[out]</td><td class="paramname">output</td><td>the convolved image </td></tr>
  </table>
  </dd>
</dl>
<dl class="section note"><dt>注解</dt><dd>if output doesn't fit in input i.e. output.rows () &lt; input.rows () or output.cols () &lt; input.cols () then output is resized to input sizes. </dd></dl>
<div class="fragment"><div class="line"><a name="l00155"></a><span class="lineno">  155</span>&#160;      {</div>
<div class="line"><a name="l00156"></a><span class="lineno">  156</span>&#160;        std::cout &lt;&lt; <span class="stringliteral">&quot;&gt;&gt;&gt; convolve cpp&quot;</span> &lt;&lt; std::endl;</div>
<div class="line"><a name="l00157"></a><span class="lineno">  157</span>&#160;        <a class="code" href="classpcl_1_1_point_cloud.html">pcl::PointCloud&lt;float&gt;</a> tmp (input.width, input.height) ;</div>
<div class="line"><a name="l00158"></a><span class="lineno">  158</span>&#160;        <a class="code" href="classpcl_1_1_gaussian_kernel.html#ad01d1002116c470278a9f4e4330eed6f">convolveRows</a> (input, horiz_kernel, tmp);        </div>
<div class="line"><a name="l00159"></a><span class="lineno">  159</span>&#160;        <a class="code" href="classpcl_1_1_gaussian_kernel.html#a13fed06781b7735db1d9461a1c507305">convolveCols</a> (tmp, vert_kernel, output);</div>
<div class="line"><a name="l00160"></a><span class="lineno">  160</span>&#160;        std::cout &lt;&lt; <span class="stringliteral">&quot;&lt;&lt;&lt; convolve cpp&quot;</span> &lt;&lt; std::endl;</div>
<div class="line"><a name="l00161"></a><span class="lineno">  161</span>&#160;      }</div>
<div class="ttc" id="aclasspcl_1_1_gaussian_kernel_html_a13fed06781b7735db1d9461a1c507305"><div class="ttname"><a href="classpcl_1_1_gaussian_kernel.html#a13fed06781b7735db1d9461a1c507305">pcl::GaussianKernel::convolveCols</a></div><div class="ttdeci">void convolveCols(const pcl::PointCloud&lt; float &gt; &amp;input, const Eigen::VectorXf &amp;kernel, pcl::PointCloud&lt; float &gt; &amp;output) const</div></div>
<div class="ttc" id="aclasspcl_1_1_gaussian_kernel_html_ad01d1002116c470278a9f4e4330eed6f"><div class="ttname"><a href="classpcl_1_1_gaussian_kernel.html#ad01d1002116c470278a9f4e4330eed6f">pcl::GaussianKernel::convolveRows</a></div><div class="ttdeci">void convolveRows(const pcl::PointCloud&lt; float &gt; &amp;input, const Eigen::VectorXf &amp;kernel, pcl::PointCloud&lt; float &gt; &amp;output) const</div></div>
<div class="ttc" id="aclasspcl_1_1_point_cloud_html"><div class="ttname"><a href="classpcl_1_1_point_cloud.html">pcl::PointCloud&lt; float &gt;</a></div></div>
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<h2 class="memtitle"><span class="permalink"><a href="#a488e6649dba1a82fcdf9414ac8aa58bc">&#9670;&nbsp;</a></span>convolve() <span class="overload">[2/2]</span></h2>

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template&lt;typename PointT &gt; </div>
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          <td class="memname">void pcl::GaussianKernel::convolve </td>
          <td>(</td>
          <td class="paramtype">const <a class="el" href="classpcl_1_1_point_cloud.html">pcl::PointCloud</a>&lt; <a class="el" href="structpcl_1_1_point_x_y_z_r_g_b_a.html">PointT</a> &gt; &amp;&#160;</td>
          <td class="paramname"><em>input</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">boost::function&lt; float(const <a class="el" href="structpcl_1_1_point_x_y_z_r_g_b_a.html">PointT</a> &amp;p)&gt;&#160;</td>
          <td class="paramname"><em>field_accessor</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">const Eigen::VectorXf &amp;&#160;</td>
          <td class="paramname"><em>horiz_kernel</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">const Eigen::VectorXf &amp;&#160;</td>
          <td class="paramname"><em>vert_kernel</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype"><a class="el" href="classpcl_1_1_point_cloud.html">pcl::PointCloud</a>&lt; float &gt; &amp;&#160;</td>
          <td class="paramname"><em>output</em>&#160;</td>
        </tr>
        <tr>
          <td></td>
          <td>)</td>
          <td></td><td> const</td>
        </tr>
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<p>Convolve a float image in the 2 directions </p><dl class="params"><dt>参数</dt><dd>
  <table class="params">
    <tr><td class="paramdir">[in]</td><td class="paramname">input</td><td>image to convolve </td></tr>
    <tr><td class="paramdir">[in]</td><td class="paramname">field_accessor</td><td>a field accessor </td></tr>
    <tr><td class="paramdir">[in]</td><td class="paramname">horiz_kernel</td><td>kernel for convolving rows </td></tr>
    <tr><td class="paramdir">[in]</td><td class="paramname">vert_kernel</td><td>kernel for convolving columns </td></tr>
    <tr><td class="paramdir">[out]</td><td class="paramname">output</td><td>the convolved image </td></tr>
  </table>
  </dd>
</dl>
<dl class="section note"><dt>注解</dt><dd>if output doesn't fit in input i.e. output.rows () &lt; input.rows () or output.cols () &lt; input.cols () then output is resized to input sizes. </dd></dl>
<div class="fragment"><div class="line"><a name="l00178"></a><span class="lineno">  178</span>&#160;      {</div>
<div class="line"><a name="l00179"></a><span class="lineno">  179</span>&#160;        std::cout &lt;&lt; <span class="stringliteral">&quot;&gt;&gt;&gt; convolve hpp&quot;</span> &lt;&lt; std::endl;</div>
<div class="line"><a name="l00180"></a><span class="lineno">  180</span>&#160;        <a class="code" href="classpcl_1_1_point_cloud.html">pcl::PointCloud&lt;float&gt;</a> tmp (input.width, input.height);</div>
<div class="line"><a name="l00181"></a><span class="lineno">  181</span>&#160;        convolveRows&lt;PointT&gt;(input, field_accessor, horiz_kernel, tmp);</div>
<div class="line"><a name="l00182"></a><span class="lineno">  182</span>&#160;        <a class="code" href="classpcl_1_1_gaussian_kernel.html#a13fed06781b7735db1d9461a1c507305">convolveCols</a>(tmp, vert_kernel, output);</div>
<div class="line"><a name="l00183"></a><span class="lineno">  183</span>&#160;        std::cout &lt;&lt; <span class="stringliteral">&quot;&lt;&lt;&lt; convolve hpp&quot;</span> &lt;&lt; std::endl;</div>
<div class="line"><a name="l00184"></a><span class="lineno">  184</span>&#160;      }</div>
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<a id="a13fed06781b7735db1d9461a1c507305"></a>
<h2 class="memtitle"><span class="permalink"><a href="#a13fed06781b7735db1d9461a1c507305">&#9670;&nbsp;</a></span>convolveCols() <span class="overload">[1/2]</span></h2>

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          <td class="memname">void pcl::GaussianKernel::convolveCols </td>
          <td>(</td>
          <td class="paramtype">const <a class="el" href="classpcl_1_1_point_cloud.html">pcl::PointCloud</a>&lt; float &gt; &amp;&#160;</td>
          <td class="paramname"><em>input</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">const Eigen::VectorXf &amp;&#160;</td>
          <td class="paramname"><em>kernel</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype"><a class="el" href="classpcl_1_1_point_cloud.html">pcl::PointCloud</a>&lt; float &gt; &amp;&#160;</td>
          <td class="paramname"><em>output</em>&#160;</td>
        </tr>
        <tr>
          <td></td>
          <td>)</td>
          <td></td><td> const</td>
        </tr>
      </table>
</div><div class="memdoc">
<p>Convolve a float image columns by a given kernel. </p><dl class="params"><dt>参数</dt><dd>
  <table class="params">
    <tr><td class="paramdir">[in]</td><td class="paramname">input</td><td>the image to convolve </td></tr>
    <tr><td class="paramdir">[in]</td><td class="paramname">kernel</td><td>convolution kernel </td></tr>
    <tr><td class="paramdir">[out]</td><td class="paramname">output</td><td>the convolved image </td></tr>
  </table>
  </dd>
</dl>
<dl class="section note"><dt>注解</dt><dd>if output doesn't fit in input i.e. output.rows () &lt; input.rows () or output.cols () &lt; input.cols () then output is resized to input sizes. </dd></dl>

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<h2 class="memtitle"><span class="permalink"><a href="#a8da5d1e51b46abcc9026006ec4eed4ba">&#9670;&nbsp;</a></span>convolveCols() <span class="overload">[2/2]</span></h2>

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template&lt;typename PointT &gt; </div>
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          <td class="memname">void pcl::GaussianKernel::convolveCols </td>
          <td>(</td>
          <td class="paramtype">const <a class="el" href="classpcl_1_1_point_cloud.html">pcl::PointCloud</a>&lt; <a class="el" href="structpcl_1_1_point_x_y_z_r_g_b_a.html">PointT</a> &gt; &amp;&#160;</td>
          <td class="paramname"><em>input</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">boost::function&lt; float(const <a class="el" href="structpcl_1_1_point_x_y_z_r_g_b_a.html">PointT</a> &amp;p)&gt;&#160;</td>
          <td class="paramname"><em>field_accessor</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">const Eigen::VectorXf &amp;&#160;</td>
          <td class="paramname"><em>kernel</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype"><a class="el" href="classpcl_1_1_point_cloud.html">pcl::PointCloud</a>&lt; float &gt; &amp;&#160;</td>
          <td class="paramname"><em>output</em>&#160;</td>
        </tr>
        <tr>
          <td></td>
          <td>)</td>
          <td></td><td> const</td>
        </tr>
      </table>
</div><div class="memdoc">
<p>Convolve a float image columns by a given kernel. </p><dl class="params"><dt>参数</dt><dd>
  <table class="params">
    <tr><td class="paramdir">[in]</td><td class="paramname">input</td><td>the image to convolve </td></tr>
    <tr><td class="paramdir">[in]</td><td class="paramname">field_accessor</td><td>a field accessor </td></tr>
    <tr><td class="paramdir">[in]</td><td class="paramname">kernel</td><td>convolution kernel </td></tr>
    <tr><td class="paramdir">[out]</td><td class="paramname">output</td><td>the convolved image </td></tr>
  </table>
  </dd>
</dl>
<dl class="section note"><dt>注解</dt><dd>if output doesn't fit in input i.e. output.rows () &lt; input.rows () or output.cols () &lt; input.cols () then output is resized to input sizes. </dd></dl>
<div class="fragment"><div class="line"><a name="l00083"></a><span class="lineno">   83</span>&#160;{</div>
<div class="line"><a name="l00084"></a><span class="lineno">   84</span>&#160;  assert(kernel.size () % 2 == 1);</div>
<div class="line"><a name="l00085"></a><span class="lineno">   85</span>&#160;  <span class="keywordtype">int</span> kernel_width = kernel.size () -1;</div>
<div class="line"><a name="l00086"></a><span class="lineno">   86</span>&#160;  <span class="keywordtype">int</span> radius = kernel.size () / 2.0;</div>
<div class="line"><a name="l00087"></a><span class="lineno">   87</span>&#160;  <span class="keywordflow">if</span>(output.<a class="code" href="classpcl_1_1_point_cloud.html#a4f34b45220c57f96607513ffad0d9582">height</a> &lt; input.height || output.<a class="code" href="classpcl_1_1_point_cloud.html#a2185a6453f8ad905d7bdf7b45754a160">width</a> &lt; input.width)</div>
<div class="line"><a name="l00088"></a><span class="lineno">   88</span>&#160;  {</div>
<div class="line"><a name="l00089"></a><span class="lineno">   89</span>&#160;    output.<a class="code" href="classpcl_1_1_point_cloud.html#a2185a6453f8ad905d7bdf7b45754a160">width</a> = input.width;</div>
<div class="line"><a name="l00090"></a><span class="lineno">   90</span>&#160;    output.<a class="code" href="classpcl_1_1_point_cloud.html#a4f34b45220c57f96607513ffad0d9582">height</a> = input.height;</div>
<div class="line"><a name="l00091"></a><span class="lineno">   91</span>&#160;    output.<a class="code" href="classpcl_1_1_point_cloud.html#af16a62638198313b9c093127c492c884">points</a>.resize (input.height * input.width);</div>
<div class="line"><a name="l00092"></a><span class="lineno">   92</span>&#160;  }</div>
<div class="line"><a name="l00093"></a><span class="lineno">   93</span>&#160; </div>
<div class="line"><a name="l00094"></a><span class="lineno">   94</span>&#160;  <span class="keywordtype">int</span> j;</div>
<div class="line"><a name="l00095"></a><span class="lineno">   95</span>&#160;  <span class="keywordflow">for</span>(<span class="keywordtype">int</span> i = 0; i &lt; input.width; i++)</div>
<div class="line"><a name="l00096"></a><span class="lineno">   96</span>&#160;  {</div>
<div class="line"><a name="l00097"></a><span class="lineno">   97</span>&#160;    <span class="keywordflow">for</span> (j = 0 ; j &lt; radius ; j++)</div>
<div class="line"><a name="l00098"></a><span class="lineno">   98</span>&#160;      output (i,j) = 0;</div>
<div class="line"><a name="l00099"></a><span class="lineno">   99</span>&#160; </div>
<div class="line"><a name="l00100"></a><span class="lineno">  100</span>&#160;    <span class="keywordflow">for</span> ( ; j &lt; input.height - radius ; j++)  {</div>
<div class="line"><a name="l00101"></a><span class="lineno">  101</span>&#160;      output (i,j) = 0;</div>
<div class="line"><a name="l00102"></a><span class="lineno">  102</span>&#160;      <span class="keywordflow">for</span> (<span class="keywordtype">int</span> k = kernel_width, l = j - radius ; k &gt;= 0 ; k--, l++)</div>
<div class="line"><a name="l00103"></a><span class="lineno">  103</span>&#160;      {</div>
<div class="line"><a name="l00104"></a><span class="lineno">  104</span>&#160;        output (i,j) += field_accessor (input (i,l)) * kernel[k];</div>
<div class="line"><a name="l00105"></a><span class="lineno">  105</span>&#160;      }</div>
<div class="line"><a name="l00106"></a><span class="lineno">  106</span>&#160;    }</div>
<div class="line"><a name="l00107"></a><span class="lineno">  107</span>&#160; </div>
<div class="line"><a name="l00108"></a><span class="lineno">  108</span>&#160;    <span class="keywordflow">for</span> ( ; j &lt; input.height ; j++)</div>
<div class="line"><a name="l00109"></a><span class="lineno">  109</span>&#160;      output (i,j) = 0;</div>
<div class="line"><a name="l00110"></a><span class="lineno">  110</span>&#160;  }</div>
<div class="line"><a name="l00111"></a><span class="lineno">  111</span>&#160;}</div>
<div class="ttc" id="aclasspcl_1_1_point_cloud_html_a2185a6453f8ad905d7bdf7b45754a160"><div class="ttname"><a href="classpcl_1_1_point_cloud.html#a2185a6453f8ad905d7bdf7b45754a160">pcl::PointCloud::width</a></div><div class="ttdeci">uint32_t width</div><div class="ttdoc">The point cloud width (if organized as an image-structure).</div><div class="ttdef"><b>Definition:</b> point_cloud.h:413</div></div>
<div class="ttc" id="aclasspcl_1_1_point_cloud_html_a4f34b45220c57f96607513ffad0d9582"><div class="ttname"><a href="classpcl_1_1_point_cloud.html#a4f34b45220c57f96607513ffad0d9582">pcl::PointCloud::height</a></div><div class="ttdeci">uint32_t height</div><div class="ttdoc">The point cloud height (if organized as an image-structure).</div><div class="ttdef"><b>Definition:</b> point_cloud.h:415</div></div>
<div class="ttc" id="aclasspcl_1_1_point_cloud_html_af16a62638198313b9c093127c492c884"><div class="ttname"><a href="classpcl_1_1_point_cloud.html#af16a62638198313b9c093127c492c884">pcl::PointCloud::points</a></div><div class="ttdeci">std::vector&lt; PointT, Eigen::aligned_allocator&lt; PointT &gt; &gt; points</div><div class="ttdoc">The point data.</div><div class="ttdef"><b>Definition:</b> point_cloud.h:410</div></div>
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<h2 class="memtitle"><span class="permalink"><a href="#ad01d1002116c470278a9f4e4330eed6f">&#9670;&nbsp;</a></span>convolveRows() <span class="overload">[1/2]</span></h2>

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          <td class="memname">void pcl::GaussianKernel::convolveRows </td>
          <td>(</td>
          <td class="paramtype">const <a class="el" href="classpcl_1_1_point_cloud.html">pcl::PointCloud</a>&lt; float &gt; &amp;&#160;</td>
          <td class="paramname"><em>input</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">const Eigen::VectorXf &amp;&#160;</td>
          <td class="paramname"><em>kernel</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype"><a class="el" href="classpcl_1_1_point_cloud.html">pcl::PointCloud</a>&lt; float &gt; &amp;&#160;</td>
          <td class="paramname"><em>output</em>&#160;</td>
        </tr>
        <tr>
          <td></td>
          <td>)</td>
          <td></td><td> const</td>
        </tr>
      </table>
</div><div class="memdoc">
<p>Convolve a float image rows by a given kernel. </p><dl class="params"><dt>参数</dt><dd>
  <table class="params">
    <tr><td class="paramdir">[in]</td><td class="paramname">kernel</td><td>convolution kernel </td></tr>
    <tr><td class="paramdir">[in]</td><td class="paramname">input</td><td>the image to convolve </td></tr>
    <tr><td class="paramdir">[out]</td><td class="paramname">output</td><td>the convolved image </td></tr>
  </table>
  </dd>
</dl>
<dl class="section note"><dt>注解</dt><dd>if output doesn't fit in input i.e. output.rows () &lt; input.rows () or output.cols () &lt; input.cols () then output is resized to input sizes. </dd></dl>

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<h2 class="memtitle"><span class="permalink"><a href="#a49ded4b4344a266ca64a76e9d7dee306">&#9670;&nbsp;</a></span>convolveRows() <span class="overload">[2/2]</span></h2>

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template&lt;typename PointT &gt; </div>
      <table class="memname">
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          <td class="memname">void pcl::GaussianKernel::convolveRows </td>
          <td>(</td>
          <td class="paramtype">const <a class="el" href="classpcl_1_1_point_cloud.html">pcl::PointCloud</a>&lt; <a class="el" href="structpcl_1_1_point_x_y_z_r_g_b_a.html">PointT</a> &gt; &amp;&#160;</td>
          <td class="paramname"><em>input</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">boost::function&lt; float(const <a class="el" href="structpcl_1_1_point_x_y_z_r_g_b_a.html">PointT</a> &amp;p)&gt;&#160;</td>
          <td class="paramname"><em>field_accessor</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">const Eigen::VectorXf &amp;&#160;</td>
          <td class="paramname"><em>kernel</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype"><a class="el" href="classpcl_1_1_point_cloud.html">pcl::PointCloud</a>&lt; float &gt; &amp;&#160;</td>
          <td class="paramname"><em>output</em>&#160;</td>
        </tr>
        <tr>
          <td></td>
          <td>)</td>
          <td></td><td> const</td>
        </tr>
      </table>
</div><div class="memdoc">
<p>Convolve a float image rows by a given kernel. </p><dl class="params"><dt>参数</dt><dd>
  <table class="params">
    <tr><td class="paramdir">[in]</td><td class="paramname">input</td><td>the image to convolve </td></tr>
    <tr><td class="paramdir">[in]</td><td class="paramname">field_accessor</td><td>a field accessor </td></tr>
    <tr><td class="paramdir">[in]</td><td class="paramname">kernel</td><td>convolution kernel </td></tr>
    <tr><td class="paramdir">[out]</td><td class="paramname">output</td><td>the convolved image </td></tr>
  </table>
  </dd>
</dl>
<dl class="section note"><dt>注解</dt><dd>if output doesn't fit in input i.e. output.rows () &lt; input.rows () or output.cols () &lt; input.cols () then output is resized to input sizes. </dd></dl>
<div class="fragment"><div class="line"><a name="l00050"></a><span class="lineno">   50</span>&#160;{</div>
<div class="line"><a name="l00051"></a><span class="lineno">   51</span>&#160;  assert(kernel.size () % 2 == 1);</div>
<div class="line"><a name="l00052"></a><span class="lineno">   52</span>&#160;  <span class="keywordtype">int</span> kernel_width = kernel.size () -1;</div>
<div class="line"><a name="l00053"></a><span class="lineno">   53</span>&#160;  <span class="keywordtype">int</span> radius = kernel.size () / 2.0;</div>
<div class="line"><a name="l00054"></a><span class="lineno">   54</span>&#160;  <span class="keywordflow">if</span>(output.<a class="code" href="classpcl_1_1_point_cloud.html#a4f34b45220c57f96607513ffad0d9582">height</a> &lt; input.height || output.<a class="code" href="classpcl_1_1_point_cloud.html#a2185a6453f8ad905d7bdf7b45754a160">width</a> &lt; input.width)</div>
<div class="line"><a name="l00055"></a><span class="lineno">   55</span>&#160;  {</div>
<div class="line"><a name="l00056"></a><span class="lineno">   56</span>&#160;    output.<a class="code" href="classpcl_1_1_point_cloud.html#a2185a6453f8ad905d7bdf7b45754a160">width</a> = input.width;</div>
<div class="line"><a name="l00057"></a><span class="lineno">   57</span>&#160;    output.<a class="code" href="classpcl_1_1_point_cloud.html#a4f34b45220c57f96607513ffad0d9582">height</a> = input.height;</div>
<div class="line"><a name="l00058"></a><span class="lineno">   58</span>&#160;    output.<a class="code" href="classpcl_1_1_point_cloud.html#af16a62638198313b9c093127c492c884">points</a>.resize (input.height * input.width);</div>
<div class="line"><a name="l00059"></a><span class="lineno">   59</span>&#160;  }</div>
<div class="line"><a name="l00060"></a><span class="lineno">   60</span>&#160; </div>
<div class="line"><a name="l00061"></a><span class="lineno">   61</span>&#160;  <span class="keywordtype">int</span> i;</div>
<div class="line"><a name="l00062"></a><span class="lineno">   62</span>&#160;  <span class="keywordflow">for</span>(<span class="keywordtype">int</span> j = 0; j &lt; input.height; j++)</div>
<div class="line"><a name="l00063"></a><span class="lineno">   63</span>&#160;  {</div>
<div class="line"><a name="l00064"></a><span class="lineno">   64</span>&#160;    <span class="keywordflow">for</span> (i = 0 ; i &lt; radius ; i++)</div>
<div class="line"><a name="l00065"></a><span class="lineno">   65</span>&#160;      output (i,j) = 0;</div>
<div class="line"><a name="l00066"></a><span class="lineno">   66</span>&#160; </div>
<div class="line"><a name="l00067"></a><span class="lineno">   67</span>&#160;    <span class="keywordflow">for</span> ( ; i &lt; input.width - radius ; i++)  {</div>
<div class="line"><a name="l00068"></a><span class="lineno">   68</span>&#160;      output (i,j) = 0;</div>
<div class="line"><a name="l00069"></a><span class="lineno">   69</span>&#160;      <span class="keywordflow">for</span> (<span class="keywordtype">int</span> k = kernel_width, l = i - radius; k &gt;= 0 ; k--, l++)</div>
<div class="line"><a name="l00070"></a><span class="lineno">   70</span>&#160;        output (i,j) += field_accessor (input (l,j)) * kernel[k];</div>
<div class="line"><a name="l00071"></a><span class="lineno">   71</span>&#160;    }</div>
<div class="line"><a name="l00072"></a><span class="lineno">   72</span>&#160; </div>
<div class="line"><a name="l00073"></a><span class="lineno">   73</span>&#160;    <span class="keywordflow">for</span> ( ; i &lt; input.width ; i++)</div>
<div class="line"><a name="l00074"></a><span class="lineno">   74</span>&#160;      output (i,j) = 0;</div>
<div class="line"><a name="l00075"></a><span class="lineno">   75</span>&#160;  }</div>
<div class="line"><a name="l00076"></a><span class="lineno">   76</span>&#160;}</div>
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<h2 class="memtitle"><span class="permalink"><a href="#a969fb903ac4abd105fead44ed59cfb49">&#9670;&nbsp;</a></span>smooth() <span class="overload">[1/2]</span></h2>

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          <td class="memname">void pcl::GaussianKernel::smooth </td>
          <td>(</td>
          <td class="paramtype">const <a class="el" href="classpcl_1_1_point_cloud.html">pcl::PointCloud</a>&lt; float &gt; &amp;&#160;</td>
          <td class="paramname"><em>input</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">const Eigen::VectorXf &amp;&#160;</td>
          <td class="paramname"><em>gaussian_kernel</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype"><a class="el" href="classpcl_1_1_point_cloud.html">pcl::PointCloud</a>&lt; float &gt; &amp;&#160;</td>
          <td class="paramname"><em>output</em>&#160;</td>
        </tr>
        <tr>
          <td></td>
          <td>)</td>
          <td></td><td> const</td>
        </tr>
      </table>
  </td>
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<span class="mlabels"><span class="mlabel">inline</span></span>  </td>
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<p>Smooth image using a gaussian kernel. </p><dl class="params"><dt>参数</dt><dd>
  <table class="params">
    <tr><td class="paramdir">[in]</td><td class="paramname">input</td><td>image </td></tr>
    <tr><td class="paramdir">[in]</td><td class="paramname">gaussian_kernel</td><td>the gaussian kernel to be used </td></tr>
    <tr><td class="paramdir">[out]</td><td class="paramname">output</td><td>the smoothed image </td></tr>
  </table>
  </dd>
</dl>
<dl class="section note"><dt>注解</dt><dd>if output doesn't fit in input i.e. output.rows () &lt; input.rows () or output.cols () &lt; input.cols () then output is resized to input sizes. </dd></dl>
<div class="fragment"><div class="line"><a name="l00241"></a><span class="lineno">  241</span>&#160;      {</div>
<div class="line"><a name="l00242"></a><span class="lineno">  242</span>&#160;        <a class="code" href="classpcl_1_1_gaussian_kernel.html#a48fcd11627d7597a27ce4f8984c9dea0">convolve</a> (input, gaussian_kernel, gaussian_kernel, output);</div>
<div class="line"><a name="l00243"></a><span class="lineno">  243</span>&#160;      }</div>
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<h2 class="memtitle"><span class="permalink"><a href="#a4ed8cb8b338735612d4e33a2f035aad9">&#9670;&nbsp;</a></span>smooth() <span class="overload">[2/2]</span></h2>

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template&lt;typename PointT &gt; </div>
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  <td class="mlabels-left">
      <table class="memname">
        <tr>
          <td class="memname">void pcl::GaussianKernel::smooth </td>
          <td>(</td>
          <td class="paramtype">const <a class="el" href="classpcl_1_1_point_cloud.html">pcl::PointCloud</a>&lt; <a class="el" href="structpcl_1_1_point_x_y_z_r_g_b_a.html">PointT</a> &gt; &amp;&#160;</td>
          <td class="paramname"><em>input</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">boost::function&lt; float(const <a class="el" href="structpcl_1_1_point_x_y_z_r_g_b_a.html">PointT</a> &amp;p)&gt;&#160;</td>
          <td class="paramname"><em>field_accessor</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">const Eigen::VectorXf &amp;&#160;</td>
          <td class="paramname"><em>gaussian_kernel</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype"><a class="el" href="classpcl_1_1_point_cloud.html">pcl::PointCloud</a>&lt; float &gt; &amp;&#160;</td>
          <td class="paramname"><em>output</em>&#160;</td>
        </tr>
        <tr>
          <td></td>
          <td>)</td>
          <td></td><td> const</td>
        </tr>
      </table>
  </td>
  <td class="mlabels-right">
<span class="mlabels"><span class="mlabel">inline</span></span>  </td>
  </tr>
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</div><div class="memdoc">
<p>Smooth image using a gaussian kernel. </p><dl class="params"><dt>参数</dt><dd>
  <table class="params">
    <tr><td class="paramdir">[in]</td><td class="paramname">input</td><td>image </td></tr>
    <tr><td class="paramdir">[in]</td><td class="paramname">field_accessor</td><td>a field accessor </td></tr>
    <tr><td class="paramdir">[in]</td><td class="paramname">gaussian_kernel</td><td>the gaussian kernel to be used </td></tr>
    <tr><td class="paramdir">[out]</td><td class="paramname">output</td><td>the smoothed image </td></tr>
  </table>
  </dd>
</dl>
<dl class="section note"><dt>注解</dt><dd>if output doesn't fit in input i.e. output.rows () &lt; input.rows () or output.cols () &lt; input.cols () then output is resized to input sizes. </dd></dl>
<div class="fragment"><div class="line"><a name="l00258"></a><span class="lineno">  258</span>&#160;      {</div>
<div class="line"><a name="l00259"></a><span class="lineno">  259</span>&#160;        convolve&lt;PointT&gt; (input, field_accessor, gaussian_kernel, gaussian_kernel, output);</div>
<div class="line"><a name="l00260"></a><span class="lineno">  260</span>&#160;      }</div>
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